Περιλαμβάνονται, με χρονολογική σειρά, δημοσιεύσεις στις οποίες έχουν χρησιμοποιηθεί δεδομένα από το εθνικό δίκτυο παρακολούθησης λιμνών.
Δημοσιεύσεις σε επιστημονικά περιοδικά
Markogianni, Vassiliki; Kalivas, Dionissios P.; Petropoulos, George P.; Giovos, Rigas; Dimitriou, Elias
Comparative Analysis of Trophic Status Assessment Using Different Sensors and Atmospheric Correction Methods in Greece’s WFD Lake Network Δημοσίευση σε επιστημονικό περιοδικό
In: Remote Sensing, vol. 17, iss. 11, no. 1822, 2025, ISSN: 2072-4292.
Περίληψη | Σύνδεσμοι | BibTeX | Ετικέτες: atmospheric correction (AC), Carlson’s Trophic State Index (TSI), GEE-platform, lake water quality, Landsat, Sentinel-2 imagery, WFD
@article{rs17111822,
title = {Comparative Analysis of Trophic Status Assessment Using Different Sensors and Atmospheric Correction Methods in Greece’s WFD Lake Network},
author = {Vassiliki Markogianni and Dionissios P. Kalivas and George P. Petropoulos and Rigas Giovos and Elias Dimitriou },
doi = {10.3390/rs17111822},
issn = {2072-4292},
year = {2025},
date = {2025-05-23},
urldate = {2025-05-23},
journal = {Remote Sensing},
volume = {17},
number = {1822},
issue = {11},
abstract = {Today, open-source Cloud Computing platforms are valuable for geospatial image analysis while the combination of the Google Earth Engine (GEE) platform and new satellite launches greatly facilitate the monitoring of national-scale lake Water Quality (WQ). The main aim of this research is to assess the transferability and performance of published general, natural-only and artificial-only lake WQ models (Chl-a, Secchi Disk Depth-SDD- and Total Phosphorus-TP) across Greece’s WFD (Water Framework Directive) lake sampling network. We utilized Landsat (7 ETM +/8 OLI) and Sentinel 2 surface reflectance (SR) data embedded in GEE, while subjected to different atmospheric correction (AC) methods. Subsequently, Carlson’s Trophic State Index (TSI) was calculated based on both in situ and modelled WQ values. Initially, WQ models employed both DOS1-corrected (Dark Object Subtraction 1; manually applied) and GEE-retrieved respective SR data from the year 2018. Double WQ values per lake station were inserted in a linear regression analysis to harmonize the AC differences, separately for Landsat and Sentinel 2 data. Yielded linear equations were accompanied by strong associations (R2 ranging from 0.68 to 0.98) while modelled and GEE-modelled TSI values were further validated based on reference in situ WQ datasets from the years 2019 and 2020. The values of the basic statistical error metrics indicated firstly the increased assessment’s accuracy of GEE-modelled over modelled TSIs and then the superiority of Landsat over Sentinel 2 data. In this way, the hereby adopted methodology was evolved into an efficient lake management tool by providing managers the means for integrated sustainable water resources management while contributing to saving valuable image pre-processing time.},
keywords = {atmospheric correction (AC), Carlson’s Trophic State Index (TSI), GEE-platform, lake water quality, Landsat, Sentinel-2 imagery, WFD},
pubstate = {published},
tppubtype = {article}
}
Today, open-source Cloud Computing platforms are valuable for geospatial image analysis while the combination of the Google Earth Engine (GEE) platform and new satellite launches greatly facilitate the monitoring of national-scale lake Water Quality (WQ). The main aim of this research is to assess the transferability and performance of published general, natural-only and artificial-only lake WQ models (Chl-a, Secchi Disk Depth-SDD- and Total Phosphorus-TP) across Greece’s WFD (Water Framework Directive) lake sampling network. We utilized Landsat (7 ETM +/8 OLI) and Sentinel 2 surface reflectance (SR) data embedded in GEE, while subjected to different atmospheric correction (AC) methods. Subsequently, Carlson’s Trophic State Index (TSI) was calculated based on both in situ and modelled WQ values. Initially, WQ models employed both DOS1-corrected (Dark Object Subtraction 1; manually applied) and GEE-retrieved respective SR data from the year 2018. Double WQ values per lake station were inserted in a linear regression analysis to harmonize the AC differences, separately for Landsat and Sentinel 2 data. Yielded linear equations were accompanied by strong associations (R2 ranging from 0.68 to 0.98) while modelled and GEE-modelled TSI values were further validated based on reference in situ WQ datasets from the years 2019 and 2020. The values of the basic statistical error metrics indicated firstly the increased assessment’s accuracy of GEE-modelled over modelled TSIs and then the superiority of Landsat over Sentinel 2 data. In this way, the hereby adopted methodology was evolved into an efficient lake management tool by providing managers the means for integrated sustainable water resources management while contributing to saving valuable image pre-processing time.